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Conference Papers Year : 2018

Parallel Pareto local search revisited – First experimental results on bi-objective UBQP

Abstract

Pareto Local Search (PLS) is a simple, yet effective optimization approach dedicated to multi-objective combinatorial optimization. It can however suffer from a high computational cost, especially when the size of the Pareto optimal set is relatively large. Recently, incorporating decomposition in PLS had revealed a high potential, not only in providing high-quality approximation sets, but also in speeding-up the search process. Using the bi-objective Unconstrained Binary Quadratic Programming (bUBQP) problem as an illustrative benchmark, we demonstrate some shortcomings in the resulting decomposition-guided Parallel Pareto Local Search (PPLS), and we propose to revisit the PPLS design accordingly. For instances with a priori unknown Pareto front shape, we show that a simple pre-processing technique to estimate the scale of the Pareto front can help PPLS to better balance the workload. Furthermore, we propose a simple technique to deal with the critically-important scalability issue raised by PPLS when deployed over a large number of computing nodes. Our investigations show that the revisited version of PPLS provides a consistent performance, suggesting that decomposition-guided PPLS can be further generalized in order to improve both parallel efficiency and approximation quality.
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Dates and versions

hal-01920339 , version 1 (23-09-2021)

Identifiers

Cite

Jialong Shi, Qingfu Zhang, Bilel Derbel, Arnaud Liefooghe, Jianyong Sun. Parallel Pareto local search revisited – First experimental results on bi-objective UBQP. GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference, Jul 2018, Kyoto, Japan. pp.753-760, ⟨10.1145/3205455.3205577⟩. ⟨hal-01920339⟩
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